Networked Document Imaging with Normalization and Optimization

  • Hirobumi Nishida
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3872)

Abstract

A system architecture is presented for document imaging in an open, distributed environment over networks, where various kinds of imaging devices can be interconnected remotely. The key components are two sets of image processing operations to transform input images to (1) canonical image representations to absorb different visual appearance due to characteristics of imaging devices or image acquisition conditions (normalization), and (2) optimal image representations according to tasks and preferences of individual users (optimization). Images captured through a diversity of input devices can be delivered to remote sites through networks, and then will be used for a variety of tasks such as printing on paper sheets, browsing on displays, and editing. These diversities can be resolved systematically by placing the normalizations at an upper end (routing servers) and the optimizations at a lower end (clients) of the data flow over networks. In view of this architecture, we describe some instances of the normalizations and optimizations associated with a particular task of highly legible printing of scanned document images. Three essential algorithms are mentioned for optimizing document images: adaptive tone mapping with background cleaning, text super-resolution, and text color clustering. The optimization process is mentioned for highly legible printing, along with some other potential applications and tasks.

Keywords

Document Image Background Color Input Device Remote Site Canonical Representation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hirobumi Nishida
    • 1
  1. 1.Document Lab, Software R&D GroupRicoh Co., Ltd.TokyoJapan

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